Universität Wien

300325 UE Multivariate statistical methods in ecology (2016W)

3.00 ECTS (2.00 SWS), SPL 30 - Biologie
Continuous assessment of course work

The course is based on the statistical language 'R'. We will offer an introduction to 'R' for people not familiar on three dates nov-jan. A general understanding of R is mandatory for th course.

Course language is English. Note combination with
VO (same name, delivered by Gabriel Singer).
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 40 participants
Language: German, English

Lecturers

Classes

Dates: An introoduction to R on three days during winter term (see below) the blocked course will take place in february (see below)

- Introduction to R (compulsary) will take place on Nov 8, Dec 6 & Jan 10
Übungsraum 6 (UZA1) 10:00-13:00

- Blocked course February 13-17 2017 13:30-17:00

Course joint with lecture (Singer, February 13-17 2017 9:00-12:00)

Information

Aims, contents and method of the course

Statistical data analysis in ecology: descriptive statistics, univariate statistical tests (ttests,
U-test, analysis of variance), bivariate data analysis (correlation, linear and nonlinear
regression), selected regression models (multiple linear regression, ANCOVA,
GLM, GAM), commonly used classic unconstrained and constrained ordination
methods: principal component analysis (PCA), canonical correspondence analysis
(CCA), redundancy analysis (RDA), distance/dissimilarity-based unconstrained and
constrained ordination methods: metric and non-metric multi-dimensional scaling
(MDS, NMDS), canonical analysis of principal coordinates (CAP), multivariate
hypothesis tests (PERMANOVA, permutation tests based on ordinations).

Assessment and permitted materials

80% presence throughout the course, participation in the team work and final
presentation are mandatory. Practical course mark is based on presence and
commitment during the course (UE 50 %), and (team-) presentations about
independently analysed dataset (UE 50 %).

Minimum requirements and assessment criteria

Successful participants will learn to apply the most commonly used statistical
methods in ecology on provided datasets. They will understand how to produce and
interpret graphical and tabular output from univariate, bivariate and multivariate
analyses of ecological datasets as presented in scientific papers and reports. They will
learn how to use the statistical software 'R'.

Examination topics

The course is scheduled as one block in February 2017 (exact date to be fixed with students) and should be attended in combination with the lecture (VO) with the same title and taking place in the same period in the mornings. The block course is followed by
independent home-based team work. Teams of 2 students each will work on specific
ecological datasets, which will be graphically and statistically analyzed under
guidance. The course then finishes with student presentations to be given during a 1-
day seminar end of February/beginning of March 2017 (exact date will be agreed
upon during the first meeting October 5, 15:00, KR Ökologie).
The course is based on the statistical language 'R'. We will offer an introduction to 'R' for people not familiar on three dates nov-jan. A general understanding of R is mandatory for th course.

Reading list

Course Handout with R-relevant information will be provided in the lecture, R-scripts
and datasets will be provided for the practical course.

Dalgaard, P. 2008. Introductory Statistics with R (Series: Statistics and Computing).
Springer Verlag, New York, 364 pp.
Borcard D., Gillet F. & Legendre P. 2011. Numerical Ecology with R. Springer, New
York, U.S.A., 306 pp.

Association in the course directory

MEC-5

Last modified: Mo 07.09.2020 15:43